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Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning
Wang, Liang1; Geng, Xin3,4; Bezdek, James2; Leckie, Christopher2; Ramamohanarao, Kotagiri2
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2010-10-01
Volume22Issue:10Pages:1401-1414
SubtypeArticle
AbstractVisual methods have been widely studied and used in data cluster analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as the VAT algorithm generally represent D as an n x n image I((D) over bar) where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is their inability to highlight cluster structure when D contains highly complex clusters. This paper addresses this limitation by proposing a Spectral VAT algorithm, where D is mapped to D' in a graph embedding space and then reordered to (D) over tilde using the VAT algorithm. A strategy for automatic determination of the number of clusters in I (D) over tilde' is then proposed, as well as a visual method for cluster formation from I((D) over tilde)' based on the difference between diagonal blocks and off-diagonal blocks. A sampling-based extended scheme is also proposed to enable visual cluster analysis for large data sets. Extensive experimental results on several synthetic and real-world data sets validate our algorithms.
KeywordClustering Vat Cluster Tendency Spectral Embedding Out-of-sample Extension
WOS HeadingsScience & Technology ; Technology
WOS KeywordLARGE DATA SETS ; ALGORITHMS ; VALIDITY ; INDEXES ; NUMBER ; AID
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000281000500005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9948
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Melbourne, Dept Comp Sci & Software Engn, Melbourne, Vic 3010, Australia
3.Southeast Univ, Sch Engn & Comp Sci, Nanjing 210096, Peoples R China
4.Monash Univ, Sch Math Sci, Clayton, Vic 3800, Australia
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Liang,Geng, Xin,Bezdek, James,et al. Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2010,22(10):1401-1414.
APA Wang, Liang,Geng, Xin,Bezdek, James,Leckie, Christopher,&Ramamohanarao, Kotagiri.(2010).Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,22(10),1401-1414.
MLA Wang, Liang,et al."Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 22.10(2010):1401-1414.
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